Latent Code-Based Fusion: A Volterra Neural Network Approach

• URL: http://arxiv.org/abs/2104.04829v1
• Date: Sat, 10 Apr 2021 18:29:01 GMT
• Title: Latent Code-Based Fusion: A Volterra Neural Network Approach
• Authors: Sally Ghanem, Siddharth Roheda, and Hamid Krim
• Abstract summary: We propose a deep structure encoder using the recently introduced Volterra Neural Networks (VNNs) We show that the proposed approach demonstrates a much-improved sample complexity over CNN-based auto-encoder with a superb robust classification performance.
• Score: 21.25021807184103
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a deep structure encoder using the recently introduced Volterra Neural Networks (VNNs) to seek a latent representation of multi-modal data whose features are jointly captured by a union of subspaces. The so-called self-representation embedding of the latent codes leads to a simplified fusion which is driven by a similarly constructed decoding. The Volterra Filter architecture achieved reduction in parameter complexity is primarily due to controlled non-linearities being introduced by the higher-order convolutions in contrast to generalized activation functions. Experimental results on two different datasets have shown a significant improvement in the clustering performance for VNNs auto-encoder over conventional Convolutional Neural Networks (CNNs) auto-encoder. In addition, we also show that the proposed approach demonstrates a much-improved sample complexity over CNN-based auto-encoder with a superb robust classification performance.

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